We attempt to learn the robust manifold learning algorithm for different applications by three approaches of de-noising, supervised learning, and unsupervised learning.

Moreover, the studying of unsupervised learning-based monitoring model lays a foundation for the applications of other unsupervised learning models in process quality control.

基于无监督学习的监控模型的研究也为其它无监督学习器在过程质量监控的应用研究奠定了基础。

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Currently, there are so many research on machine learning algorithm, and machine learing algorithm can be divided into supervised and unsupervised.

目前基于机器学习算法的研究较多，大致可以分为有监督和无监督学习两类。

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The paper puts forward the method that based on the neural network unsupervised learning, also, improves the index on separation effects.

提出基于神经网络无监督学习的盲分离方法，并改进了分离效果评判指标。

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Classification accuracy of unsupervised learning algorithm is usually not satisfactory, so people tend to make use of supervised learning in practice.

由于无监督学习的准确率通常不能令人满意，在实际应用中人们趋向于运用有监督方法。

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The paper presents an unsupervised learning algorithm to learn answer pattern for answer extraction module of Chinese Question Answering ( QA).

本文提出了一种基于无监督学习算法的问答模式抽取技术从互联网上抽取应用于汉语问答系统的答案模式。

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The results show that the convergence of improved algorithm is faster and the decision making is valid based on topology map mode.

计算结果表明，改进的无监督学习算法收敛速度快，基于拓扑映射图模型的多属性决策有效。

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The traditional clustering is based on unsupervised learning mechanism, partitioning objects on the basis of a specific distance or similarity.

传统的聚类算法基于无监督学习机制，仅依据某种特定的距离或相似度进行划分。

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The pre-knowledge of the probability density function is used in the noise sample's classification, clustering and unsupervised parameter estimation.

通过对噪声样本进行数据挖掘和模式识别，在线学习噪声的实时特性，获得其概率密度函数的先验知识，应用此先验知识进行样本聚类、分类、无监督学习并对噪声参数进行精确估计。

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As an important unsupervised learning algorithm without pre-marked samples in machine learning, clustering has been well developed in recent years.

聚类作为机器学习中一种重要的无监督学习算法，具有无需事前标记样本类型等优点，在近些年得到了很好地发展和应用。

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The process of clustering is dividing the not training samples into different meaningful class clusters, which belongs to unsupervised learning.

聚类过程是将没有训练样本的数据集划分为有意义的不同类，属于无监督学习。

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Semi-supervised clustering is to use a small amount of labeled samples and unlabeled samples to guide the clustering process.

半监督聚类也就是无监督学习，就是使用少量的标记样本对无标记样本的聚类过程进行指导。

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Unsupervised learning is used to adjust input weight values and supervised learning is utilized to adjust output weight values.

学习过程中，采用无监督学习算法对输入权重进行调整，采用有监督学习算法对输出权重进行调整。

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The supervised and unsupervised learning diagnosis methods are discussed and several improvements have been presented in the learning algorithms.

本文对模拟故障诊断的有监督学习和无监督学习方法分别进行了研究，通过对实现过程的分析，对经典的学习算法进行深入研究，并提出若干改进。

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As an unsupervised learning method, cluster analysis is one of the most important research fields in machine learning.

聚类分析作为一种无监督学习方法，是机器学习领域重要研究方向之一。

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From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning.

从机器学习的观点来看，类相当于隐藏模式，寻找类是无监督学习过程。

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We can derive a self-organizing learning algorithm that maximizes the information transferred in a network of nonlinear units.

信息最大化方法将信息理论与神经网络相结合，根据使网络中所传递的信息量最大，推导出一种无监督学习准则。

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The traditional cluster is unsupervised learn-ing, that is, it does't need the prior knowledge.

传统的聚类是无监督学习，也就是说不需要先验知识。

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Constrained clustering and transductive learning mainly deal with learning problems between unsupervised learning and supervised learning.

约束聚类和约束分类主要处理学习问题的方式间于无监督学习和监督学习。

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Semi-supervised learning flourishes as it can circumvent the limitations of unsupervised learning and supervised learning.

半监督学习的蓬勃发展规避了无监督学习和监督学习的局限性。

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Multiple Attribute Information Decision Making Mode and Application Via Improved Fast Unsupervised Learning Algorithm

多属性信息决策的改进无监督学习算法建模与应用

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